The Overlap of Genetic Susceptibility to Schizophrenia and Cardiometabolic Disease Can Be Used to Identify Metabolically Diferent Groups of Individuals Rona J
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www.nature.com/scientificreports OPEN The overlap of genetic susceptibility to schizophrenia and cardiometabolic disease can be used to identify metabolically diferent groups of individuals Rona J. Strawbridge1,2,3*, Keira J. A. Johnston1,4,5, Mark E. S. Bailey5, Damiano Baldassarre6,7, Breda Cullen1, Per Eriksson3, Ulf deFaire8, Amy Ferguson1,9, Bruna Gigante3, Philippe Giral10, Nicholas Graham1, Anders Hamsten3, Steve E. Humphries11, Sudhir Kurl12, Donald M. Lyall1, Laura M. Lyall1, Jill P. Pell1, Matteo Pirro13, Kai Savonen14,15, Andries J. Smit16, Elena Tremoli7, Tomi‑Pekka Tomainen17, Fabrizio Veglia7, Joey Ward1, Bengt Sennblad18 & Daniel J. Smith1 Understanding why individuals with severe mental illness (Schizophrenia, Bipolar Disorder and Major Depressive Disorder) have increased risk of cardiometabolic disease (including obesity, type 2 diabetes and cardiovascular disease), and identifying those at highest risk of cardiometabolic disease are important priority areas for researchers. For individuals with European ancestry we explored whether genetic variation could identify sub‑groups with diferent metabolic profles. Loci associated with schizophrenia, bipolar disorder and major depressive disorder from previous genome‑wide association studies and loci that were also implicated in cardiometabolic processes and diseases were selected. In the IMPROVE study (a high cardiovascular risk sample) and UK Biobank (general population sample) multidimensional scaling was applied to genetic variants implicated in both psychiatric and cardiometabolic disorders. Visual inspection of the resulting plots used to identify distinct clusters. Diferences between these clusters were assessed using chi‑squared and Kruskall‑Wallis tests. In IMPROVE, genetic loci associated with both schizophrenia and cardiometabolic disease (but not bipolar disorder or major depressive disorder) identifed three groups of individuals with distinct metabolic profles. This grouping was replicated within UK Biobank, with somewhat less distinction between metabolic profles. This work focused on individuals of European ancestry and is unlikely 1Institute of Health and Wellbeing, University of Glasgow, Room 111, Public Health, 1 Lilybank Gardens, Glasgow G12 8RZ, UK. 2Health Data Research, London, UK. 3Cardiovascular Medicine Unit, Department of Medicine Solna, Karolinska Institute, Stockholm, Sweden. 4Deanery of Molecular, Genetic and Population Health Sciences, College of Medicine and Veterinary Medicine, University of Edinburgh, Edinburgh, Scotland, UK. 5School of Life Sciences, College of Medical, Veterinary & Life Sciences, University of Glasgow, Glasgow, Scotland, UK. 6Department of Medical Biotechnology and Translational Medicine, Universit degli Studi di Milano, Milan, Italy. 7Centro Cardiologico Monzino, IRCCS, Milan, Italy. 8Cardiovascular and Nutritional Epidemiology, Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden. 9Usher Institute, University of Edinburgh, Edinburgh, UK. 10Service Endocrinologie-Metabolisme, Groupe Hôpitalier Pitie-Salpetriere, Unités de Prévention Cardiovasculaire, Assistance Publique - Hopitaux de Paris, Paris, France. 11Centre for Cardiovascular Genetics, Institute Cardiovascular Science, University College London, London, UK. 12Institute of Public Health and Clinical Nutrition, University of Eastern Finland, Kuopio, Finland. 13Internal Medicine, Angiology and Arteriosclerosis Diseases, Department of Clinical and Experimental Medicine, University of Perugia, Perugia, Italy. 14Foundation for Research in Health Exercise and Nutrition, Kuopio Research Institute of Exercise Medicine, Kuopio, Finland. 15Department of Clinical Physiology and Nuclear Medicine, Kuopio University Hospital, Kuopio, Finland. 16Department of Medicine, University Medical Center Groningen and University of Groningen, Groningen, The Netherlands. 17Public Health and Clinical Nutrition, Department of Medicine, University of Eastern Finland, Kupiou, Finland. 18Department of Cell and Molecular Biology, National Bioinformatics Infrastructure Sweden, Science for Life Laboratory, Uppsala University, Uppsala, Sweden. *email: [email protected] Scientifc Reports | (2021) 11:632 | https://doi.org/10.1038/s41598-020-79964-x 1 Vol.:(0123456789) www.nature.com/scientificreports/ to apply to more genetically diverse populations. Overall, this study provides proof of concept that common biology underlying mental and physical illness may help to stratify subsets of individuals with diferent cardiometabolic profles. Individuals with serious mental illness (such as schizophrenia (SCZ), major depressive disorder (MDD) and bipolar disorder (BD)) have a reduced life expectancy (10–15 years for BD, 15–20 years for SCZ1). Tis is likely due to the well-established increased prevalence of cardiovascular and metabolic disorders compared to the general population. For example, obesity is up to 3.5-fold higher in those with SCZ2, type 2 diabetes is ~ twofold higher in those with MDD, BD or SCZ 2, and cerebrovascular disease is increased by up to 3.3-fold in those with BD2. Understanding this increased risk and identifying individuals at highest risk of metabolic and cardiovascular disease are important priority areas for researchers and healthcare providers. Historically, the increased risk and prevalence of cardiometabolic disease (CMD) has been attributed to social determinants and lifestyle factors (including poor diet, sedentary behaviour, alcohol and substance use) that co-exist with serious mental illness and efects of psychotropic medication2, however there is growing evidence that there might be common biological mechanisms underlying both mental and psychiatric illness. As genetic data is stable over an individual’s lifetime, and not infuenced by disease course, genetic approaches are ideal for investigation of common biology in comorbid conditions. Te identifcation of genetic variants robustly associ- ated with a wide range of psychiatric and cardiometabolic phenotypes by international genetics consortia has enabled the exploration of relationships between psychiatric and cardiometabolic conditions. Genome-wide genetic correlations between psychiatric and cardiometabolic traits provide evidence for underlying common biology. Correlations have been described between depression and obesity (rg = 0.12) or cardiovascular disease (rg = 0.42)3. Evidence of causal relationships between psychiatric and cardiometabolic traits have also been described 1,4,5. However, the mechanisms involved have yet to be uncovered and therefore this knowledge has had no clinical impact. Here we tested whether a novel approach using multi-dimensional scaling (MDS) of genetic variation asso- ciated with psychiatric and cardiometabolic disorders could aid stratifcation of individuals into groups with difering cardiometabolic risk profles. Results The IMPROVE and UK Biobank studies. Te demographic characteristics of the IMPROVE, UK Biobank subsets 1 (UKB1) and 2 (UKB2) are provided in Table 1. At baseline, individuals in IMPROVE (a Euro- pean high cardiovascular-risk cohort) were older, more overweight and more likely to have T2D, hypertension or medication for hypertension or lipid-lowering medication than the UKB subsets (self-reported white British general population cohort). UKB1 and UKB2 were very similar, with lower frequency of hypertension at follow- up in UKB1 (51.5%) compared to UKB2 (62.0%) but slightly larger carotid Intima-media thickness (cIMT, indicative of vessel wall remodelling) measures in UKB2 to UKB1. Despite diferent proportions of UKB1 and UKB2 completing the mental health questionnaire, the frequencies of BD, MDD and GAD were similar. Figure 1 provides a schematic overview of the analysis procedure. SCZ‑CM loci can identify metabolically distinct groups of individuals in IMPROVE. When using IMPROVE and single nucleotide polymorphisms (SNPs) with a minor allele frequency (MAF) > 1%, implicated in both SCZ and CMD (SCZ-CMD), plotting the frst two multi-dimensional scaling components (C1 and C2) demonstrated 3 groups of individuals (by visual inspection) (Fig. 2a). Separation was predominantly due to C1, and whilst C1 is nominally signifcantly correlated with latitude (rho = − 0.036, p = 0.0339), the clustering is not being driven by latitude (Supplementary Fig. 1). SNPs with MAF as low as 1% might difer across popula- tions (even within the same ancestry grouping), therefore robustness to MAF threshold also assessed. When using MAF > 5% showed additional groups (Fig. 2b), whereas MAF > 10% showed similar groups to MAF > 1% (Fig. 2c). Assignment to groups was consistent using MAF > 1% and MAF > 10% (Supplementary Table 1). Te three groups appear to have modest diferences in cardiometabolic profles (Table 2): Group 3 had a signifcantly lower frequency of hypertension (group 3: 74% vs groups 1 or 2: 80% or 81% respectively, P = 0.004) and lower fastest progression of cIMT (group 3: 0.156 mm vs groups 1 or 2: 0.176 mm or 0.166 mm, P = 0.002). Tis is sur- prising given the (non-signifcant) higher rates of smoking in this group. Group 2 had (non-signifcantly) lower rates of T2D than the other groups (group 2: 25% vs groups 1 or 3: 28%). Similar groups were observed using T-distributed Stochastic Neighbour Embedding (tSNE) or principal component analyses (PCA, Supplementary Methods), with the majority of individuals being consistently grouped together (Supplementary Figs. 2 and 3, respectively). Tis result appears specifc to SCZ-CMD SNP subset;